

import argparse
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from PIL import Image

from transformers import GLPNConfig, GLPNForDepthEstimation, GLPNImageProcessor
from transformers.utils import logging


logging.set_verbosity_info()
__snake_case				:      Optional[Any]   		=			logging.get_logger(__name__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Any 							= OrderedDict()
			for key, value in state_dict.items():
						if key.startswith("""module.encoder"""):
									a_						:						List[str] 							= key.replace("""module.encoder"""  ,	"""glpn.encoder""")
						if key.startswith("""module.decoder"""):
									a_						:						List[Any] 							= key.replace("""module.decoder"""  ,	"""decoder.stages""")
						if "patch_embed" in key:
									# replace for example patch_embed1 by patch_embeddings.0
									a_						:						Dict 							= key[key.find("""patch_embed""") + len("""patch_embed""")]
									a_						:						Optional[int] 							= key.replace(f'''patch_embed{idx}'''  ,	f'''patch_embeddings.{int(a__)-1}''')
						if "norm" in key:
									a_						:						Dict 							= key.replace("""norm"""  ,	"""layer_norm""")
						if "glpn.encoder.layer_norm" in key:
									# replace for example layer_norm1 by layer_norm.0
									a_						:						Dict 							= key[key.find("""glpn.encoder.layer_norm""") + len("""glpn.encoder.layer_norm""")]
									a_						:						List[Any] 							= key.replace(f'''layer_norm{idx}'''  ,	f'''layer_norm.{int(a__)-1}''')
						if "layer_norm1" in key:
									a_						:						Optional[int] 							= key.replace("""layer_norm1"""  ,	"""layer_norm_1""")
						if "layer_norm2" in key:
									a_						:						Dict 							= key.replace("""layer_norm2"""  ,	"""layer_norm_2""")
						if "block" in key:
									# replace for example block1 by block.0
									a_						:						List[Any] 							= key[key.find("""block""") + len("""block""")]
									a_						:						List[str] 							= key.replace(f'''block{idx}'''  ,	f'''block.{int(a__)-1}''')
						if "attn.q" in key:
									a_						:						Optional[Any] 							= key.replace("""attn.q"""  ,	"""attention.self.query""")
						if "attn.proj" in key:
									a_						:						Tuple 							= key.replace("""attn.proj"""  ,	"""attention.output.dense""")
						if "attn" in key:
									a_						:						Optional[Any] 							= key.replace("""attn"""  ,	"""attention.self""")
						if "fc1" in key:
									a_						:						Dict 							= key.replace("""fc1"""  ,	"""dense1""")
						if "fc2" in key:
									a_						:						int 							= key.replace("""fc2"""  ,	"""dense2""")
						if "linear_pred" in key:
									a_						:						Any 							= key.replace("""linear_pred"""  ,	"""classifier""")
						if "linear_fuse" in key:
									a_						:						Tuple 							= key.replace("""linear_fuse.conv"""  ,	"""linear_fuse""")
									a_						:						int 							= key.replace("""linear_fuse.bn"""  ,	"""batch_norm""")
						if "linear_c" in key:
									# replace for example linear_c4 by linear_c.3
									a_						:						Union[str, Any] 							= key[key.find("""linear_c""") + len("""linear_c""")]
									a_						:						Optional[int] 							= key.replace(f'''linear_c{idx}'''  ,	f'''linear_c.{int(a__)-1}''')
						if "bot_conv" in key:
									a_						:						int 							= key.replace("""bot_conv"""  ,	"""0.convolution""")
						if "skip_conv1" in key:
									a_						:						int 							= key.replace("""skip_conv1"""  ,	"""1.convolution""")
						if "skip_conv2" in key:
									a_						:						Optional[Any] 							= key.replace("""skip_conv2"""  ,	"""2.convolution""")
						if "fusion1" in key:
									a_						:						List[Any] 							= key.replace("""fusion1"""  ,	"""1.fusion""")
						if "fusion2" in key:
									a_						:						List[Any] 							= key.replace("""fusion2"""  ,	"""2.fusion""")
						if "fusion3" in key:
									a_						:						int 							= key.replace("""fusion3"""  ,	"""3.fusion""")
						if "fusion" in key and "conv" in key:
									a_						:						str 							= key.replace("""conv"""  ,	"""convolutional_layer""")
						if key.startswith("""module.last_layer_depth"""):
									a_						:						Optional[int] 							= key.replace("""module.last_layer_depth"""  ,	"""head.head""")
						a_						:						int 							= value

			return new_state_dict


def 					_UpperCAmelCase				(						a__  ,	a__):

			'''simple docstring'''



			for i in range(config.num_encoder_blocks):
						for j in range(config.depths[i]):
									# read in weights + bias of keys and values (which is a single matrix in the original implementation)
									a_						:						List[Any] 							= state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.weight''')
									a_						:						Any 							= state_dict.pop(f'''glpn.encoder.block.{i}.{j}.attention.self.kv.bias''')
									# next, add keys and values (in that order) to the state dict
									a_						:						Optional[int] 							= kv_weight[
									    : config.hidden_sizes[i], :
									]
									a_						:						List[Any] 							= kv_bias[: config.hidden_sizes[i]]
									a_						:						Union[str, Any] 							= kv_weight[
									    config.hidden_sizes[i] :, :
									]
									a_						:						Optional[Any] 							= kv_bias[config.hidden_sizes[i] :]


def 					_UpperCAmelCase				(						):

			'''simple docstring'''



			a_						:						Union[str, Any] 							= """http://images.cocodataset.org/val2017/000000039769.jpg"""
			a_						:						List[Any] 							= Image.open(requests.get(a__  ,	stream=a__).raw)

			return image


@torch.no_grad()
def 					_UpperCAmelCase				(						a__  ,	a__  ,	a__=False  ,	a__=None):

			'''simple docstring'''



			a_						:						Union[str, Any] 							= GLPNConfig(hidden_sizes=[6_4, 1_2_8, 3_2_0, 5_1_2]  ,	decoder_hidden_size=6_4  ,	depths=[3, 8, 2_7, 3])

			# load image processor (only resize + rescale)
			a_						:						List[str] 							= GLPNImageProcessor()

			# prepare image
			a_						:						Dict 							= prepare_img()
			a_						:						Optional[Any] 							= image_processor(images=a__  ,	return_tensors="""pt""").pixel_values

			logger.info("""Converting model...""")

			# load original state dict
			a_						:						Any 							= torch.load(a__  ,	map_location=torch.device("""cpu"""))

			# rename keys
			a_						:						Tuple 							= rename_keys(a__)

			# key and value matrices need special treatment
			read_in_k_v(a__  ,	a__)

			# create HuggingFace model and load state dict
			a_						:						Union[str, Any] 							= GLPNForDepthEstimation(a__)
			model.load_state_dict(a__)
			model.eval()

			# forward pass
			a_						:						Optional[Any] 							= model(a__)
			a_						:						Dict 							= outputs.predicted_depth

			# verify output
			if model_name is not None:
						if "nyu" in model_name:
									a_						:						Optional[Any] 							= torch.tensor(
									    [[4.4147, 4.0873, 4.0673], [3.7890, 3.2881, 3.1525], [3.7674, 3.5423, 3.4913]])
						elif "kitti" in model_name:
									a_						:						Optional[int] 							= torch.tensor(
									    [[3.4291, 2.7865, 2.5151], [3.2841, 2.7021, 2.3502], [3.1147, 2.4625, 2.2481]])
						else:
									raise ValueError(f'''Unknown model name: {model_name}''')

						a_						:						Any 							= torch.Size([1, 4_8_0, 6_4_0])

						assert predicted_depth.shape == expected_shape
						assert torch.allclose(predicted_depth[0, :3, :3]  ,	a__  ,	atol=1e-4)
						print("""Looks ok!""")

			# finally, push to hub if required
			if push_to_hub:
						logger.info("""Pushing model and image processor to the hub...""")
						model.push_to_hub(
						    repo_path_or_name=Path(a__  ,	a__)  ,	organization="""nielsr"""  ,	commit_message="""Add model"""  ,	use_temp_dir=a__  ,	)
						image_processor.push_to_hub(
						    repo_path_or_name=Path(a__  ,	a__)  ,	organization="""nielsr"""  ,	commit_message="""Add image processor"""  ,	use_temp_dir=a__  ,	)


if __name__ == "__main__":
	__snake_case				:      List[Any]   		=			argparse.ArgumentParser()

	parser.add_argument(
	    """--checkpoint_path""",
	    default=None,
	    type=str,
	    help="""Path to the original PyTorch checkpoint (.pth file).""",
	)
	parser.add_argument(
	    """--pytorch_dump_folder_path""", default=None, type=str, help="""Path to the folder to output PyTorch model."""
	)
	parser.add_argument(
	    """--push_to_hub""", action="""store_true""", help="""Whether to upload the model to the HuggingFace hub."""
	)
	parser.add_argument(
	    """--model_name""",
	    default="""glpn-kitti""",
	    type=str,
	    help="""Name of the model in case you're pushing to the hub.""",
	)
	__snake_case				:      Union[str, Any]   		=			parser.parse_args()
	convert_glpn_checkpoint(args.checkpoint_path, args.pytorch_dump_folder_path, args.push_to_hub, args.model_name)





import math


def 					_UpperCAmelCase				(						a__ = 1_0_0):

			'''simple docstring'''



			a_						:						List[str] 							= sum(i * i for i in range(1  ,	n + 1))
			a_						:						Optional[Any] 							= int(math.pow(sum(range(1  ,	n + 1))  ,	2))
			return square_of_sum - sum_of_squares


if __name__ == "__main__":
	print(F"""{solution() = }""")


